A bit-chain based algorithm for problem of attribute reduction

Abstract

Rough set is a widespread concept in computer science and is applicable in many fields such as artificial intelligence, expert systems, data mining, pattern recognition and decision support systems. One of key problems of knowledge acquisition in theoretical study of rough sets is attribute reduction. Attribute reduction also called feature selection eliminates superfluous attributes in the information system and improves efficiency of data analysis process. But reducing attributes is a NP-hard problem. Recently, to overcome the technical difficulty, there are a lot of research on new approaches such as maximal tolerance classification (Fang Yang et al. 2010), genetic algorithm (N. Ravi Shankar et al. 2010), topology and measure of significance of attributes (P.G. JansiRani and R. Bhaskaran 2010), soft set (Tutut Herawan et al. 2010), positive approximation (Yuhua Qian et al. 2010), dynamic programming (Walid Moudani et al. 2010). However, there are still some challenging research issues that time consumption is still hard problem in attribute reduction. This paper introduces a new approach with a model presented with definitions, theorems, operations. Set of maximal random prior forms is put forward as an effective way for attribute reduction. The algorithm for seeking maximal random prior set are proposed with linear complexity, contributes to solve absolutely problems in attribute reduction and significantly improve the speed of calculation and data analysis. © 2012 Springer-Verlag.